Data driven smart non-pneumatic tires

Abstract

Various embodiments of a smart non-pneumatic tire and methods for generating a mean vibration characteristic of the smart non-pneumatic tire are described. In one embodiment, a method for measuring the mean vibration characteristic includes receiving tire-road contact acceleration data from an accelerometer that is secured near a tire-surface contact region, where the tire-road contact acceleration data includes data captured by the accelerometer over a duration of time while a tread along an outer periphery of a sector of the tire contacts a surface. The method further includes receiving velocity data for the tire and load data for the tire over the duration of time. The method further includes generating a mean vibration characteristic based on the above-mentioned data. The method also includes changing a stiffness of spokes of the tire based on the generated mean vibration characteristic in some cases.

Therefore, at least the following is claimed: 
     
         1 . A method for measuring a vibration characteristic of a non-pneumatic tire, comprising:
 receiving, by a computing device, tire-road contact acceleration data from an accelerometer, the accelerometer being secured to a spoke of a non-pneumatic tire within a sector of the tire, wherein the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface;   receiving, by the computing device, velocity data for the tire over the duration of time;   receiving, by the computing device, normal load data for the tire over the duration of time; and   generating, by the computing device, a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data.   
     
     
         2 . The method of  claim 1 , further comprising generating a transfer function or a frequency response function for the tire based on the mean vibration characteristic for the tire. 
     
     
         3 . The method of  claim 1 , wherein:
 the normal load data, the velocity data, and the tire-road contact acceleration data comprise dynamic tire data; and   the method further comprises chopping the dynamic tire data into a plurality of per-revolution data subsets, a per-revolution data subset among the plurality of per-revolution data subsets comprising dynamic tire data for one revolution of the tire.   
     
     
         4 . The method of  claim 3 , further comprising removing outlier data from the plurality of per-revolution data subsets. 
     
     
         5 . The method of  claim 4 , further comprising feeding the plurality of per-revolution data subsets into a vibration modeler for generating the mean vibration characteristic for the tire. 
     
     
         6 . The method of  claim 1 , wherein the tire comprises a second accelerometer secured within the tire at a location closer to a center of the tire than to the accelerometer, the second accelerometer being communicatively coupled to the computing device and configured to transmit tire-center acceleration data to the computing device over the duration of time while the tread extending along the outer periphery of the sector contacts the surface. 
     
     
         7 . The method of  claim 6 , further comprising comparing the mean vibration characteristic for the tire to the tire-center acceleration data. 
     
     
         8 . The method of  claim 1 , wherein generating the mean vibration characteristic for the tire comprises applying a machine learning algorithm to the tire-road contact acceleration data, the velocity data, and the normal load data. 
     
     
         9 . The method of  claim 8 , wherein the machine learning algorithm comprises a decision tree and bootstrapped aggregation. 
     
     
         10 . The method of  claim 8 , wherein the machine learning algorithm comprises a neural network. 
     
     
         11 . The method of  claim 1 , wherein generating the mean vibration characteristic for the tire comprises performing a frequency domain analysis on the tire-road contact acceleration data, the velocity data, and the normal load data. 
     
     
         12 . The method of  claim 1 , wherein the tire comprises a plurality of spokes, the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli. 
     
     
         13 . The method of  claim 12 , wherein the smart material comprises piezoelectric material. 
     
     
         14 . The method of  claim 12 , wherein the method further comprises changing a stiffness of the plurality of spokes based on the mean vibration characteristic for the tire. 
     
     
         15 . The method of  claim 1 , wherein the sector comprises an area enclosed by a region of the tire defined between a contact patch angle having a vertex at a center of the tire, radii extending from the contact patch angle to a circular arc extending along the outer periphery of the tire between the radii, and the circular arc. 
     
     
         16 . The method of  claim 15 , wherein the accelerometer is secured to the spoke at a position closer to the circular arc than to the center of the tire. 
     
     
         17 . The method of  claim 15 , wherein the mean vibration characteristic for the tire comprises an average vibration characteristic estimated at the center of the tire, and the tire-road contact acceleration data comprises vibration characteristics measured while the tread along the outer periphery of the sector of the tire contacts the surface. 
     
     
         18 . A smart non-pneumatic tire, comprising:
 an accelerometer, the accelerometer being secured to a spoke of the non-pneumatic tire within a sector of the tire; and   a computing device communicatively coupled to the accelerometer, the computing device configured to:
 receive tire-road contact acceleration data from the accelerometer, wherein the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface; 
 receive velocity data for the tire over the duration of time; 
 receive normal load data for the tire over the duration of time; and 
 generate a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data. 
   
     
     
         19 . The smart non-pneumatic tire of  claim 18 , wherein the sector comprises an area enclosed by a region of the tire defined between a contact patch angle having a vertex at a center of the tire, radii extending from the contact patch angle to a circular arc extending along the outer periphery of the tire between the radii, and the circular arc. 
     
     
         20 . The smart non-pneumatic tire of  claim 18 , wherein the tire comprises a plurality of spokes, the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli.